IndoCEISS
Diabetic Wound Segmentation Using Masking Contour Image Processing
Abstract
Measuring the wound area in diabetics is still using a manual way with a wound ruler. Whereas the ruler affixed to the wound will become a contaminated agent that can transmit the infection to other recipients. Digital measurement methods are needed to solve the problem. However, clarifying the boundaries between the wound and the skin requires carefulness and high accuracy. For this reason, it has needed an imaging method that can do segmentation between the wound and the skin boundary for diabetic patients based on digital, called digital planimetry. This study uses a masking contour image processing algorithm from the Hue, Saturation, Value (HSV), Then doing iteration five times and gamma filter. So the result of segmentation is formed. This study concludes that the segmentation with this method has not been able to perform the segment properly, and it requires more masking values, but the results of the 5th iteration got a minor error, which is 0.002%. The digital imaging carried out in this study could be developed to be a digital-based diabetic patient wound measurement tool.
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Imelda, S. I., 2019, Faktor-Faktor Yang Mempengaruhi Terjadinya diabetes Melitus di Puskesmas Harapan Raya Tahun 2018, Scientia Journal, Vol. 8, Ed. 1, 28–39, https://doi.org/10.35141/scj.v8i1.406
IDF Diabetes Atlas Ninth edition 2019, 2019, International Diabetes Federation, https://www.diabetesatlas.org/en/, diakses tgl January 21, 2019
Schaper, N. C., Van Netten, J. J., Apelqvist, J., Lipsky, B. A., & Bakker, K., 2016, Prevention and management of foot problems in diabetes: a Summary Guidance for Daily Practice 2015, based on the IWGDF Guidance Documents, Diabetes/Metabolism Research and Reviews, Vol. 32, Ed. 1, 7–15, https://doi.org/10.1002/dmrr.2695
Bus, Si. A., Waaijman, R., Arts, M., Haart, M. De, Busch-Westbroek, T., Van Baal, J., & Nollet, F., 2013, Effect of custom-made footwear on foot ulcer recurrence in diabetes: A multicenter randomized controlled trial, Diabetes Care, Vol. 36, Ed. 12, 4109–4116, https://doi.org/10.2337/dc13-0996
Sulistyo, A. A. H., 2018, Management of Diabetic Foot Ulcer: a Literature Review, Jurnal Keperawatan Indonesia, Vol. 21, Ed. 2, 84–93, https://doi.org/10.7454/jki.v21i2.634
Williams, K. J., Sounderajah, V., Dharmarajah, B., Thapar, A., & Davies, A. H., 2017, Simulated Wound Assessment Using Digital Planimetry versus Three-Dimensional Cameras: Implications for Clinical Assessment, Annals of Vascular Surgery, Vol. 41, 235–240, https://doi.org/10.1016/j.avsg.2016.10.029
Jørgensen, L. B., Sørensen, J. A., Jemec, G. B. E., & Yderstr, K. B., 2015, Methods to assess area and volume of wounds -- a systematic review, Statistical Methods, 14,
Foltynski, P., 2018, Ways to increase precision and accuracy of wound area measurement using smart devices: Advanced app Planimator, PLoS ONE, Vol. 13, Ed. 3, 1–16, https://doi.org/10.1371/journal.pone.0192485
Babu, K. S., Ravi Kumar, Y. B., & Sabut, S., 2017, An improved watershed segmentation by flooding and pruning algorithm for assessment of diabetic wound healing, RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings, Vol. 2018- Janua, 679–683, https://doi.org/10.1109/RTEICT.2017.8256683
Ahmad Fauzi, M. F., Khansa, I., Catignani, K., Gordillo, G., Sen, C. K., & Gurcan, M. N., 2015, Computerized segmentation and measurement of chronic wound images, Computers in Biology and Medicine, Vol. 60, 74–85, https://doi.org/10.1016/j.compbiomed.2015.02.015
Niu, S., Chen, Q., de Sisternes, L., Ji, Z., Zhou, Z., & Rubin, D. L., 2017, Robust noise region-based active contour model via local similarity factor for image segmentation, Pattern Recognition, Vol. 61, 104–119, https://doi.org/10.1016/j.patcog.2016.07.022
Xiong, W., Yu, J., Lin, Z., Yang, J., Lu, X., Barnes, C., & Luo, J., n.d., Foreground-aware Image Inpainting, 2, 5840–5848,
Wang, L., Chang, Y., Wang, H., Wu, Z., Pu, J., & Yang, X., 2017, An active contour model based on local fitted images for image segmentation, Information Sciences, Vol. 418–419, 61–73, https://doi.org/10.1016/j.ins.2017.06.042
Gerhana, Y. A., Zulfikar, W. B., Ramdani, A. H., & Ramdhani, M. A., 2018, Implementation of Nearest Neighbor using HSV to Identify Skin Disease, IOP Conference Series: Materials Science and Engineering, Vol. 288, Ed. 1, https://doi.org/10.1088/1757-899X/288/1/012153
Shi, R. B., Qiu, J., & Maida, V., 2019, Towards algorithm-enabled home wound monitoring with smartphone photography: A hue-saturation-value colour space thresholding technique for wound content tracking, International Wound Journal, Vol. 16, Ed. 1, 211–218, https://doi.org/10.1111/iwj.13011
op ‘t Veld, R. C., van den Boomen, O. I., Lundvig, D. M. S., Bronkhorst, E. M., Kouwer, P. H. J., Jansen, J. A., Middelkoop, E., Von den Hoff, J. W., Rowan, A. E., & Wagener, F. A. D. T. G., 2018, Thermosensitive biomimetic polyisocyanopeptide hydrogels may facilitate wound repair, Biomaterials, Vol. 181, 392–401, https://doi.org/10.1016/j.biomaterials.2018.07.038
Grandi, V., Bacci, S., Corsi, A., Sessa, M., Puliti, E., Murciano, N., Scavone, F., Cappugi, P., & Pimpinelli, N., 2018, ALA-PDT exerts beneficial effects on chronic venous ulcers by inducing changes in inflammatory microenvironment, especially through increased TGF-beta release: A pilot clinical and translational study, Photodiagnosis and Photodynamic Therapy, Vol. 21, 252–256, https://doi.org/10.1016/j.pdpdt.2017.12.012
Sirazitdinova, E., & Deserno, T. M., 2017, System design for 3D wound imaging using low-cost mobile devices, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, Vol. 10138, 1013810, https://doi.org/10.1117/12.2254389
RGB to HSV color conversion, n.d., Retrieved March 13, 2020, from https://www.rapidtables.com/convert/color/rgb-to-hsv.html, diakses tgl March 13, 2020
Gupta, A., 2017, Real time wound segmentation/management using image processing on handheld devices, Journal of Computational Methods in Sciences and Engineering, Vol. 17, Ed. 2, 321–329, https://doi.org/10.3233/JCM-170706
Fauzan, A., 2015, Ruang Warna Hue Saturation Value (HSV) serta proses Konversinya, Kita Infromatika, http://www.kitainformatika.com/2015/01/ruang-warna-hue-saturation-value-hsv.html, diakses tgl March 24, 2021
Bozorgtabar, B., Sedai, S., Kanti Roy, P., & Garnavi, R., 2017, Skin lesion segmentation using deep convolution networks guided by local unsupervised learning, IBM Journal of Research and Development, Vol. 61, Ed. 4, 1–8, https://doi.org/10.1147/JRD.2017.2708283
Mishra, R., & Daescu, O., 2017, Deep learning for skin lesion segmentation, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1189–1194, https://doi.org/10.1109/BIBM.2017.8217826
Li, F., Wang, C., Liu, X., Peng, Y., & Jin, S., 2018, A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks, Computational Intelligence and Neuroscience, Vol. 2018, Ed. 1, https://doi.org/10.1155/2018/4149103
Hamuda, E., Mc Ginley, B., Glavin, M., & Jones, E., 2017, Automatic crop detection under field conditions using the HSV colour space and morphological operations, Computers and Electronics in Agriculture, Vol. 133, 97–107, https://doi.org/10.1016/j.compag.2016.11.021
Kumar, M., & Jindal, S. R., 2019, Fusion of RGB and HSV colour space for foggy image quality enhancement, Multimedia Tools and Applications, Vol. 78, Ed. 8, 9791–9799, https://doi.org/10.1007/s11042-018-6599-8
Jawahar, M., Jani Anbarasi, L., Graceline Jasmine, S., & Narendra, M., 2020, Diabetic foot ulcer segmentation using color space models, Proceedings of the 5th International Conference on Communication and Electronics Systems, ICCES 2020, Icces, 742–747, https://doi.org/10.1109/ICCES48766.2020.09138024
Elmogy, M., Khalil, A., Shalaby, A., Mahmoud, A., Ghazal, M., & El-Baz, A., 2019, Chronic Wound Healing Assessment System Based on Color and Texture Analysis, IST 2019 - IEEE International Conference on Imaging Systems and Techniques, Proceedings, https://doi.org/10.1109/IST48021.2019.9010586
Li, Y., Zhang, J., Gao, P., Jiang, L., & Chen, M., 2018, Grab Cut Image Segmentation Based on Image Region, 2018 3rd IEEE International Conference on Image, Vision and Computing, ICIVC 2018, 311–315, https://doi.org/10.1109/ICIVC.2018.8492818
Rother, C., Kolmogorov, V., & Blake, A., 2004, GrabCut - Interactive foreground extraction using iterated graph cuts, ACM SIGGRAPH 2004 Papers, SIGGRAPH 2004, 309–314, https://doi.org/10.1145/1186562.1015720
Salam, E. J., 2017, K-{Means} {Clustering} {Algorithm}, In K-Means Clustering Algorithm, https://ekojunaidisalam.com/2017/02/09/k-means-clustering-algorithm/,
Estri, M. N., Nurshiami, S. R., Reorita, R., & Ibrohim, M. O., 2018, Penentuan kriteria penghentian iterasi pada algoritma stroberi, Jurnal Ilmiah Matematika Dan Pendidikan Matematika, Vol. 10, Ed. 1, 27, https://doi.org/10.20884/1.jmp.2018.10.1.2834
DOI: http://dx.doi.org/10.30700/jst.v11i2.1114
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